ABSTRACT Reducing HIV incidence requires a ?precision public health? approach encompassing prevention campaigns, targeted interventions, and ?next-generation? surveillance through multimodal instruments, including sequencing. Molecular epidemiology methods (phylogenetics and phylodynamics) have recently gained traction for use in identifying and tracking epidemic transmission clusters, as well as reconstructing the demographic history of viral pathogen populations. However, such methods are not equipped to identify both transmission clusters and their corresponding dynamics in real time, and transmission clusters are assumed to be unrealistically static over the course of the epidemic. Consequently, scientists, and/or clinicians, cannot distinguish between growth or decline of a sub-epidemic nor determine the sources ?local or external? driving regional epidemic incidence. The overarching goal of this project is to develop a novel theoretical and technical framework able to model dynamically HIV transmission clusters over time by analyzing large, longitudinal sequence data. Our new tool for dynamic identification of transmission epicenters (HIV-DYNAMITE) will aid in monitoring, in real time, hyper-connected and/or long-lasting clusters fueling emerging HIV regional epidemics. As an application study, we will focus on the ongoing HIV epidemic in Florida. Florida has the highest incidence of HIV in the United States (US) due to a unique set of circumstances fostering an ongoing statewide epidemic. HIV infections are rising amongst men who have sex with men, minority, and immigrant/tourist populations. Risk assessment of the epidemic thus needs to be contextualized, taking into account both local determinants as well as inflow from other states and/or countries. In collaboration with the Florida Department of Health (FDOH), we seek to identify and predict infection trends as well as virus spread within Florida and among neighboring destinations by applying HIV-DYNAMITE to the extensive FDOH viral sequence dataset (>44,300 sequences) collected over the past 10 years coupled with geo-demographic information. We will validate the system using newly obtained longitudinal data and design intervention strategies through focus groups with the FDOH and other stakeholders. We aim to both complement and enhance existing efforts, such as the CDC?s HIV-TRACE, which is currently based on sequence data only, which also lacks dynamic or geographic spread components. The interdisciplinary expertise of the team renders this project feasible, and the proposed approach has the potential to be incorporated into other settings within the US with comparable statewide surveillance and virus sequencing coverage through national reference centers.